model = dict(
    type='DBNet',
    # backbone=dict(
    #     type='mmedt.ResNet',
    #     depth=50,
    #     num_stages=4,
    #     out_indices=(0, 1, 2, 3),
    #     frozen_stages=-1,
    #     norm_cfg=dict(type='BN', requires_grad=True),
    #     norm_eval=False,
    #     style='pytorch',
    #     dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
    #     # init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
    #     stage_with_dcn=(False, True, True, True)),
    backbone=dict(
        type='CLIPResNet', # CLIPResNetWithAttention CLIPResNet
        pretrained='/apdcephfs/private_v_fisherwyu/code/OCRCLIP/ocrclip/pretrained/RN50.pt',
        layers=[3, 4, 6, 3],
        output_dim=1024,
        input_resolution=640, # 512
        style='pytorch'),
    neck=dict(
        type='FPNC', in_channels=[256, 512, 1024, 2048], lateral_channels=256),
    bbox_head=dict(
        type='DBHead',
        in_channels=256,
        loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True),
        postprocessor=dict(type='DBPostprocessor', text_repr_type='quad')),
    train_cfg=None,
    test_cfg=None)
